{"id":"W2014291971","doi":"10.1287/inte.1090.0448","title":"Optimization Helps Shermag Gain Competitive Edge","year":2009,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Supply chain; Supply chain optimization; Procurement; Competitive advantage; Supply chain network; Software; Component (thermodynamics); Computer science; Market share; Total cost; Operations research; Supply chain management; Engineering; Business; Marketing","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001156791,0.0007230028,0.0005539804,0.0009882043,0.0006024365,0.00253747,0.0006498763,0.0008067756,0.01333658],"category_scores_gemma":[0.003536214,0.0002671161,0.0004302447,0.001229282,0.0004059991,0.001820991,0.0009620287,0.0009406338,0.001834217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001356705,"about_ca_system_score_gemma":0.002263086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004636839,"about_ca_topic_score_gemma":0.01286696,"domain_scores_codex":[0.9992889,0.0002298214,0.00002429388,0.0000911805,0.0002232043,0.0001424709],"domain_scores_gemma":[0.9987707,0.0006869289,0.0001019743,0.0001054916,0.000204271,0.0001307259],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001067177,0.0009282588,0.01179037,0.0003177067,0.00008876604,0.0003581928,0.0003675508,0.2607131,0.008903872,0.07445376,0.05416062,0.5868506],"study_design_scores_gemma":[0.0001864288,0.0003285323,0.003877629,0.00007502088,0.00006314651,0.0001326819,0.0008206648,0.8596702,0.006742559,0.05147173,0.07658812,0.00004336264],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4226217,0.001487335,0.3701763,0.01227929,0.0002823859,0.0003915664,0.00103841,0.002511303,0.1892118],"genre_scores_gemma":[0.7709714,0.0006951756,0.2087571,0.0007764333,0.00006803285,0.00009346518,0.000785374,0.0003045594,0.01754847],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01333658,"threshold_uncertainty_score":0.04461527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01019653572668703,"score_gpt":0.2230742850534684,"score_spread":0.2128777493267814,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}